Data Mart Interview Questions & Answers

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If you are an expert in Data Mart then this is for you. Let us know if you looking for a job change? Then do not worry, we’ve a right answer for your job interview preparation. If you are preparing for Data Mart job interview then go through Wisdomjobs interview questions and answers page. Data Mart is the structure to data warehousing where data can be retrieved from database. It is considered to be repository of data. It is a subject-oriented database and is also known as High Performance Query Structures (HPQS). There are certifications to improve the visibility in interview. Good knowledge on the Data Mart is must to crack the interview. Below are the Data Mart interview questions and answers which makes you comfortable to face the interviews:

Data Marts is used on a business division/department level. A data mart only contains the required subject specific data for local analysis. A database, or collection of databases, designed to help managers make strategic decisions about their business. data marts are usually smaller and focus on a particular subject or department. Some data marts, called dependent data marts, are subsets of large data warehouses. A data mart is a simpler form of a data warehouse focused on a single subject (or functional area) such as sales, finance, marketing, HR etc. Data Mart represents data from single business process.

Metadata is data about data. E.g. if in data mart we are receiving any file. Then metadata will contain information like how many columns, file is fix width/limited, ordering of fields, data types of field etc.

Metadata is data about data. meta data comes in picture when we need to know about how data is stored and where it is stored. metadata tool is helpful in capturing the business meta data and the following sections explain business metadata. metadata tools are using for gathering, storing, updating, and for retrieving the business and technical metadata of an org.

Data validation strategies are often heavily influenced by the architecture for the application. If the application is already in production it will be significantly harder to build the optimal architecture than if the application is still in a design stage. If a system takes a typical architectural approach of providing common services then one common component can filter all input and output thus optimizing the rules and minimizing efforts.

There are three main models to think about when designing a data validation strategy:

Accept Only Known Valid Data

Reject Known Bad Data

Sanitize Bad Data

We cannot emphasize strongly enough that Accept Only Known Valid Data is the best strategy. We do however recognize that this isn't always feasible for political financial or technical reasons and so we describe the other strategies as well.

Datamart is something which consists of only one type of data whereas datwarehouse consists of data of different type.For example all the organisation data say data related to finance department,HR,Banking dept are stored in datawarehouse whereas in datamart say only finance data will stored.So datawarehouse is a collection of different datamarts.

A data mart is a subject oriented database which supports the business needs of individual departments within the enterprise.It is an subset of the enterprise data warehouse.It is also known as high performance query structures.

Data validation is generally done manually in DWH in this case if source and TGT are relational you need to create SQL scripts to validate source and target data and if source is Flat file or non relational database you can use excel if data is very less or create dummy tables to validate your ETL code.

Data validation is generally done manually in DWH in this case if source and TGT are relational you need to create SQL scripts to validate source and target data and if source is Flat file or non relational database you can use excel if data is very less or create dummy tables to validate your ETL code.

Data validation strategies are often heavily influenced by the architecture for the application. If the application is already in production it will be significantly harder to build the optimal architecture than if the application is still in a design stage. If a system takes a typical architectural approach of providing common services then one common component can filter all input and output thus optimizing the rules and minimizing efforts.

Data warehouse is made up of many datamarts. DWH contain many subject areas. However, data mart focuses on one subject area generally. E.g. If there will be DHW of bank then there can be one data mart for accounts, one for Loans etc. This is high-level definitions.